datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
apex-agents
APEX–Agents
APEX–Agents is a benchmark from Mercor for evaluating whether AI agents can execute long-horizon, cross-application professional services tasks. Tasks were created by investment banking analysts, management consultants, and corporate lawyers, and require agents to navigate realistic work environments with files and tools (e.g., docs, spreadsheets, PDFs, email, chat, calendar).
Tasks: 480 total (160 per job category)
Worlds: 33 total (10 banking, 11 consulting, 12… See the full description on the dataset page: https://huggingface.co/datasets/mercor/apex-agents.dcvlm-baseline-200b
DCVLM-Baseline (200B tokens)
DCVLM-Baseline is the reference training mixture from our DataComp-VLM paper.
It is a pre-mixed, decontaminated, ready-to-train multimodal pretraining dataset, materialized as flat
WebDataset tar shards so it can be consumed by any training
stack.
This is a 200B-token dataset release consisting of 103,985,276 samples, curated from our DCVLM-large data pool.
A smaller 6.25B-token version is also available.
⚠️ NOTE: The training data is the WebDataset… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/dcvlm-baseline-200b.witLLaVA-OneVision-2-Data
LLaVA-OneVision-2-Data
Training data for the LLaVA-OneVision-2 multimodal model family. The release contains large-scale video data at several duration ranges, video captions and source mappings, and spatial-reasoning data used for mid-training.
At a Glance
The dataset is split across two Hugging Face repositories because of its size:
Repository
What it contains
Part 1 (this repository)
~60-second video shards, captions for all duration ranges… See the full description on the dataset page: https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-2-Data.mesh4d_datasetMegaPairs-Standard
MegaPairs-Standard (Standardized Version)
Dataset Summary
This is a standardized, high-efficiency version of the JUNJIE99/MegaPairs dataset.
Why use this version?
The original dataset is distributed as a massive Tar archive containing millions of images, accompanied by a separate JSONL annotation file.
The Problem: Using the original format requires extracting terabytes of small files (which can exhaust disk inodes) or writing complex logic to read from archives. It… See the full description on the dataset page: https://huggingface.co/datasets/86Cao/MegaPairs-Standard.samplesOpenMind
The OpenMind Dataset: A large-scale Head-And-Neck 3D MRI Dataset for self-supervised learning
Description
The OpenMind Dataset is a large-scale 3D MRI dataset of the head and neck region featuring 114k MRI Images. Its purpose is to provide access of large amounts of 3D medical imaging data to accelerate the development of self-supervised learning methods for 3D medical imaging. This data was pooled from exactly 800 datasets from the OpenNeuro platform and… See the full description on the dataset page: https://huggingface.co/datasets/MIC-DKFZ/OpenMind.monet
Dataset Card for MONET
MONET (Massive, Open, Non-redundant and Enriched Text-to-image dataset) is a large-scale, curated image-text dataset designed for training text-to-image (T2I) systems. It contains 103.8 million high-quality image-text pairs distilled from 2.9 billion raw pairs across nine heterogeneous open sources (6 real and 3 synthetic) through successive stages of safety filtering, domain-based filtering, exact and near-duplicate removal, and re-captioning with… See the full description on the dataset page: https://huggingface.co/datasets/jasperai/monet.MDCdatacomp_pools
DataComp Pools
This repository contains metadata files for DataComp. For details on how to use the metadata, please visit our website and our github repository.
We distribute the image url-text samples and metadata under a standard Creative Common CC-BY-4.0 license. The individual images are under their own copyrights.
Terms and Conditions
We have terms of service that are similar to those adopted by HuggingFace (https://huggingface.co/terms-of-service), which covers… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/datacomp_pools.MulSeT
MulSeT: A Benchmark for Multi-view Spatial Understanding Tasks
Paper: Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
Code: https://github.com/WanyueZhang-ai/spatial-understanding
A high-level overview of the MulSeT benchmark. The dataset challenges models to integrate information from two distinct viewpoints of a 3D scene to answer spatial reasoning questions.
📝 Dataset Summary
MulSeT is a comprehensive benchmark… See the full description on the dataset page: https://huggingface.co/datasets/WanyueZhang/MulSeT.FOMO260K
FOMO260K: Brain MRI Dataset for Large-Scale Self-Supervised Learning with Clinical Data
Dataset paper preprint: A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.
https://arxiv.org/pdf/2506.14432.
Description
FOMO260K is a large-scale dataset of brain MRI scans, including both clinical and research-grade scans. The dataset includes a wide range of sequences, including T1, MPRAGE, T2, T2*, FLAIR, SWI, T1c, PD, DWI… See the full description on the dataset page: https://huggingface.co/datasets/FOMO-MRI/FOMO260K.MathNet
Quick Start · Overview · Tasks · Comparison · Dataset Stats · Data Sources · Pipeline · Schema · License · Citation
This is the official MathNet v0. A larger version v1 will be uploaded soon (more countires, problems and richer metadata). Schema is stable but field values may be revised in v1.
Quick start
from datasets import load_dataset
# Default: all problems
ds = load_dataset("ShadenA/MathNet", split="train")
# Or a specific country / competition-body config… See the full description on the dataset page: https://huggingface.co/datasets/ShadenA/MathNet.MMMU
MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)
🌐 Homepage | 🏆 Leaderboard | 🤗 Dataset | 🤗 Paper | 📖 arXiv | GitHub
🔔News
🛠️[2026-07-10]: Fixed incorrect ground-truth answer labels in validation_Design_15 and validation_Art_Theory_4.
🛠️[2026-04-21]: Fixed option issue in test_Psychology_15.
‼️[2026-02-12]: We have released the answers for the test set! You can now evaluate your models on the test set… See the full description on the dataset page: https://huggingface.co/datasets/MMMU/MMMU.mnist
Dataset Card for MNIST
Dataset Summary
The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class.
Half of the image were drawn by Census Bureau employees and the other half by high school students… See the full description on the dataset page: https://huggingface.co/datasets/ylecun/mnist.VDR_MEGA_MultiDomain_DocRetrieval
Visual Document Retrieval Dataset
Overview
This dataset is designed for training visual document retrieval models. It combines multiple datasets from the VDR series, Colpali, and LlamaIndex to create the most comprehensive training resource for visual document retrieval tasks.
Dataset Structure
The dataset contains structured fields including unique identifiers with string lengths ranging from 45 to 50 characters, search query text with variable lengths between… See the full description on the dataset page: https://huggingface.co/datasets/racineai/VDR_MEGA_MultiDomain_DocRetrieval.MolmoAct-Midtraining-Mixture
MolmoAct - Midtraining Mixture
Data Mixture used for MolmoAct Midtraining. Contains MolmoAct Dataset formulated as Action Reasoning Data.
MolmoAct is a fully open-source action reasoning model for robotic manipulation developed by the Allen Institute for AI. MolmoAct is trained on a subset of OXE and MolmoAct Dataset, a dataset with 10k high-quality trajectories of a single-arm Franka robot performing 93 unique manipulation tasks in both home and tabletop environments. It has… See the full description on the dataset page: https://huggingface.co/datasets/allenai/MolmoAct-Midtraining-Mixture.LLaVA-OneVision-1.5-Instruct-Data
LLaVA-OneVision-1.5 Instruction Data
Paper | Code
📌 Introduction
This dataset, LLaVA-OneVision-1.5-Instruct, was collected and integrated during the development of LLaVA-OneVision-1.5. LLaVA-OneVision-1.5 is a novel family of Large Multimodal Models (LMMs) that achieve state-of-the-art performance with significantly reduced computational and financial costs. This meticulously curated 22M instruction dataset (LLaVA-OneVision-1.5-Instruct) is part of a… See the full description on the dataset page: https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-1.5-Instruct-Data.cad-environments
CAD Environments
CAD Environments is a multimodal dataset of complete, human-performed workflows in desktop CAD software. The current release contains 51 task workflows totaling 99.03 hours, covering eight software groups across mechanical design, architecture, MEP, structural design, and general 3D modeling.
Each workflow preserves the full task context—not just the final model—including the problem statement, reference and input files, a gold output, evaluation rubrics, a… See the full description on the dataset page: https://huggingface.co/datasets/markov-ai/cad-environments.coco-2017-mirror
COCO 2017 mirror
This is a just mirror of the raw COCO dataset files, for convenience. You have to download it using something like:
pip install huggingface_hub
huggingface-cli download --local-dir coco-2017 pcuenq/coco-2017-mirror
And then unzip the files before use.
imagefolder_with_metadatavarious
malcolmrey's Various AI Model, Architecture & Research Repository
Welcome to the central research and asset repository of malcolmrey. This repository hosts cutting-edge tools, custom architectures, RefMod latent adapter systems, video synthesis engines, training configurations, benchmark suites, cinematic scripts, and comprehensive educational guides spanning MiniMax-H3, FLUX.2 / Klein 9B, WAN 2.1, LTX-Video, Z-Image, SDXL, and Stable Diffusion.
🧭 Repository Map &… See the full description on the dataset page: https://huggingface.co/datasets/malcolmrey/various.dataposterGenEvolve-Data-Bench
GenEvolve Data and Bench
This repository contains the open-source data release for GenEvolve:
Config
Directory
Records
Images
Purpose
sft
GenEvolve-Data-SFT/
9,000 trajectories
50,291 reference images
supervised cold-start trajectories
rl
GenEvolve-Data-RL/
3,175 prompts
3,175 GT images
self-evolution / RL training prompts
bench
GenEvolve-Bench/
594 prompts
594 GT images
held-out evaluation benchmarkAll metadata is provided in both JSONL and Parquet. The Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/MeiGen-AI/GenEvolve-Data-Bench.my-storagedatacomp_xlarge
DataComp XLarge Pool
This repository contains metadata files for the xlarge pool of DataComp. For details on how to use the metadata, please visit our website and our github repository.
We distribute the image url-text samples and metadata under a standard Creative Common CC-BY-4.0 license. The individual images are under their own copyrights.
Terms and Conditions
We have terms of service that are similar to those adopted by HuggingFace… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/datacomp_xlarge.documentation-mediaMMMUThis is a merged version of MMMU/MMMU with all subsets concatenated.
Large-scale Multi-modality Models Evaluation Suite
Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval
🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets
This Dataset
This is a formatted version of MMMU. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@article{yue2023mmmu,
title={Mmmu: A… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/MMMU.